Object Segmentation Pseudo-Labels from Extreme-Point Clicks
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Solution Overview
Problem
Creating labeled training data for machine learning, particularly for medical image analysis, is a long and costly process, often resulting in insufficient data, which hinders effective object detection and segmentation.
Innovation Solution
A method using minimal user interaction, such as clicking extreme points on an object of interest, to generate pseudo-labels for training a segmentation model, combined with a random walker algorithm and a fully convolutional network, allows for efficient generation of training datasets for medical image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to create labeled training data, then the quality and accuracy of training data is improved, but the time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service annotation where the segmentation model automatically generates pseudo-labels for training data without requiring manual annotation for every sample. The model uses user clicks as weak supervision signals to autonomously create labeled training datasets, significantly reducing annotation time while maintaining acceptable quality through iterative refinement
Solution Approach 2:
The system performs preliminary action by pre-segmenting images using the trained model to generate pseudo-labels before actual training occurs. This pre-processing step creates initial training data that can be quickly generated and iteratively improved, avoiding the need for time-consuming manual annotation of all training samples
2Measurement precision
If manual annotation is used to create labeled training data, then the quality of training data is improved, but the cost and complexity of the process increase
Solution Approach 1:
The segmentation model serves itself by automatically generating training data with pseudo-labels using minimal user input (clicks on extreme points). This self-service mechanism simplifies the overall annotation process complexity while maintaining data quality through the model's learned segmentation capabilities and iterative refinement
Solution Approach 2:
The system introduces an intermediary approach where user clicks on extreme points serve as weak supervision signals that guide the pre-segmentation process. These minimal user inputs mediate between completely automated annotation and full manual annotation, reducing process complexity while maintaining acceptable quality
3Productivity
If minimal user interaction is used to generate pseudo-labels, then the productivity and speed of training data generation is improved, but the annotation precision may decrease
Solution Approach 1:
The system performs preliminary pre-segmentation using the trained model to generate pseudo-labels quickly from user clicks. This preliminary action produces initial training data at high speed, which is then used to retrain and refine the model, creating an iterative improvement cycle that maintains precision while achieving high productivity
Solution Approach 2:
The system employs periodic action through iterative retraining cycles where the model is continuously refined using newly generated pseudo-labeled data. This periodic refinement process progressively improves label accuracy while maintaining high productivity, as each iteration builds upon previous results rather than requiring complete manual re-annotation
Data Source
AI summary
Apparatuses, systems, and techniques to detect object in images including digital representations of those objects. In at least one embodiment, one or more objects are detected in an image based, at least in part, on points corresponding to a surface of one or more objects.